On the effect of multi-parents recombination in binary coded genetic algorithms

The recombination operator plays a very important role in genetic algorithms. We present binary coded genetic algorithms in which more than two parents are involved in the recombination operation. We propose two types of multi-parent recombination operators, the multi-cut (MX) and seed crossover (SX). Each of these operators is a natural generalization of two parents recombination operator. These operators are evaluated on the De Jong standard test functions. The results showed clearly that the multi-parent recombinations lead to better performance, although the performance improvement for different techniques were found to be dependent on problems.

[1]  Michael de la Maza,et al.  Book review: Genetic Algorithms + Data Structures = Evolution Programs by Zbigniew Michalewicz (Springer-Verlag, 1992) , 1993 .

[2]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[3]  Nostrand Reinhold,et al.  the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .

[4]  A. E. Eiben,et al.  Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms , 1995, ECAL.

[5]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[6]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[7]  Jim Smith,et al.  Recombination strategy adaptation via evolution of gene linkage , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[8]  Alden H. Wright,et al.  Genetic Algorithms for Real Parameter Optimization , 1990, FOGA.

[9]  Lakhmi C. Jain,et al.  Automatic generation of a neural network architecture using evolutionary computation , 1995, Proceedings Electronic Technology Directions to the Year 2000.

[10]  A. E. Eiben,et al.  Genetic algorithms with multi-parent recombination , 1994, PPSN.

[11]  Shigeyoshi Tsutsui,et al.  A study on the effect of multi-parent recombination in real coded genetic algorithms , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[12]  A. E. Eiben,et al.  Multi-Parent's Niche: n-ary Crossovers on NK-Landscapes , 1996, PPSN.

[13]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[14]  Kenneth A. De Jong,et al.  Using Problem Generators to Explore the Effects of Epistasis , 1997, ICGA.

[15]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[16]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[17]  H. Muhlenbein,et al.  Gene pool recombination and utilization of covariances for the Breeder Genetic Algorithm , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[18]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[19]  Hans-Georg Beyer,et al.  Toward a Theory of Evolution Strategies: On the Benefits of Sex the (/, ) Theory , 1995, Evolutionary Computation.

[20]  Lakhmi C. Jain,et al.  Automatic Generation of Neural Network Architecture Using Evolutionary Computation , 1997, Advances in Fuzzy Systems - Applications and Theory.